System, method and recording medium for cognitive proximates

ABSTRACT

A cognitive proximate recommendation method, system, and non-transitory computer readable medium, include identifying a requested item based on a user request, first extracting a requested feature and a requested value of the requested feature for the requested item, and returning a return item from a plurality of return items stored in the database by: second extracting a return feature corresponding to the requested feature for each of the plurality of return items, third extracting a return value of the return feature, and calculating a proximal distance between the return value for each of the plurality of return items and the requested value of the requested item.

BACKGROUND

The present invention relates generally to a cognitive proximaterecommendation method, and more particularly, but not by way oflimitation, to a system, method, and recording medium for recommendingan item in response to a user query having a lowest proximal distancebetween values of extracted features of the items and the requested itemby the user.

Industry is trending towards so called “cognitive models” enabled via“Big Data” platforms. Such cognitive models are aimed to remember priorinteractions with users and continuously learn and refine the responsesfor future interactions. For example, cognitive agents are being usedfor welcoming customers at business door steps and are expected toevolve intelligent with generations. Such agents could be enriched forbetter customer handling by building the intelligence of the agents.

Conventional cognitive models for searching and returning answers haveproposed searching for information within social networks. Theconventional search assist techniques receive a query, such as a partialquery, identifies two or more categories of data that includeinformation satisfying the query, ranks the identified categories ofdata based on various selection criteria, and presents suggested searchterms based on the rankings. However, the conventional techniques relateto a display of the results, not the selection in that the conventionaltechniques rank the results of the query on two or more identifiedcategories and calculate a quality matrix that is used to displayresults. The conventional techniques do not intelligently learn toprovide best alternatives when a null response may occur.

That is, there is a technical problem in that the conventionaltechniques do not consider a cognitive way of determining a bestalternative when a match does not exist and do not consider using userpreferences to weigh values of features of potential results tointelligently provide a better alternative.

SUMMARY

Thus, the inventors have realized a technical solution to the technicalproblem to provide significantly more than the conventional technique ofquestion/answer interaction by configuring a cognitive analysis ofrequested items by extracting the requested features and values of thefeatures by the user and intelligently providing a closest alternativebased on extracting the same features of alternative items and comparingthe values of the alternatives with user preferences to return theclosest alternative. Thus, the technical solution improves upon thecomputer functionality itself by providing better results moreefficiently.

In an exemplary embodiment, the present invention can provide acognitive proximate recommendation method including a database, themethod including identifying a requested item based on a user request,first extracting a requested feature and a requested value of therequested feature for the requested item, and returning a return itemfrom a plurality of return items stored in the database by: secondextracting a return feature corresponding to the requested feature foreach of the plurality of return items, third extracting a return valueof the return feature, and calculating a proximal distance between thereturn value for each of the plurality of return items and the requestedvalue of the requested item.

Further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording acognitive proximate recommendation program including a database, theprogram causing a computer to perform: identifying a requested itembased on a user request, first extracting a requested feature and arequested value of the requested feature for the requested item, andreturning a return item from a plurality of return items stored in thedatabase by: second extracting a return feature corresponding to therequested feature for each of the plurality of return items, thirdextracting a return value of the return feature, and calculating aproximal distance between the return value for each of the plurality ofreturn items and the requested value of the requested item.

Even further, in another exemplary embodiment, the present invention canprovide a cognitive proximate recommendation system, said systemincluding a database, a processor, and a memory, the memory storinginstructions to cause the processor to: identifying a requested itembased on a user request, first extracting a requested feature and arequested value of the requested feature for the requested item, andreturning a return item from a plurality of return items stored in thedatabase by: second extracting a return feature corresponding to therequested feature for each of the plurality of return items, thirdextracting a return value of the return feature, and calculating aproximal distance between the return value for each of the plurality ofreturn items and the requested value of the requested item.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a high level flow chart for a cognitiveproximate recommendation method 100.

FIG. 2 exemplarily shows a high level flow chart for at least Step 105of the cognitive proximate recommendation method 100.

FIG. 3 exemplarily shows one embodiment of method 100.

FIG. 4 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 5 depicts a cloud computing environment according to anotherembodiment of the present invention.

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-6, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity. Exemplary embodiments are provided below forillustration purposes and do not limit the claims.

With reference now to FIG. 1, the cognitive proximate recommendationmethod 100 includes various steps to provide a user with a closest itemhaving a lowest (smallest) proximal distance from the user request.Moreover, the method (system) can benefit from “learning” from pastpreferences of the user. As shown in at least FIG. 4, one or morecomputers of a computer system 12 can include a memory 28 havinginstructions stored in a storage system to perform the steps of FIG. 1.

With the use of these various steps and instructions, the cognitiveproximate recommendation method 100 may act in a more sophisticated anduseful fashion, and in a cognitive manner while giving the impression ofmental abilities and processes related to knowledge, attention, memory,judgment and evaluation, reasoning, and advanced computation. That is, asystem is said to be “cognitive” if it possesses macro-scaleproperties—perception, goal-oriented behavior, learning/memory andaction—that characterize systems (i.e., humans) that all agree arecognitive.

Although as shown in FIGS. 4-6 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing circuit which may execute in a layer thecognitive proximate recommendation system method (FIG. 5), it is notedthat the present invention can be implemented outside of the cloudenvironment.

Step 101 receives a user request of a question, query, input, search orthe like that the user would like a return. Step 101 can receive theuser input by, for example, a Graphical User Interface (GUI)-basedinterface enabling the CRUD (Create, Read, Update, Delete) processes andextension of an industry specific proximate framework. The user request101 includes a description of the item (e.g., item description 140).

Based on the user request of Step 101, Step 102 identifies the requesteditem based on the domain-specific taxonomy database 130 and itemdescription 140.

The domain specific taxonomy 130 includes information on lexicalrelations between words or ontological relations between concepts suchthat the item description 140 can be used to identify the item requestedin Step 102 and identify features and values of the requested item andpotential return items in Steps 103 and 104 as described later. Thedomain-specific taxonomy 130 can be cognitive in that thedomain-specific taxonomy 130 can learn new lexical relations betweenwords based on the user choosing a response not ranked the highest.

For example, if the user request of Step 101 is “I want a red car with a2.0-cylinder engine under 20,000 dollars” (e.g., the item description),Step 102 identifies that the “item” is “a car” that the user isrequesting.

After the item is identified by Step 102, Step 103 extracts the features(attributes) of the requested item and the values of the features inputby the user. For example, Step 103 can extract from the user input of “Iwant a red car with a 2.0-cylinder engine under 20,000 dollars” thefeature of “color” having a value of “red”, the feature of “cost” havinga value of “under 20,000 dollars”, and the feature of “engine size”having a value of “2.0-cylinder”.

That is, each feature comprises one or more values. Moreover, eachfeature (or value of a feature) can be dependent or independent of otherfeatures (or values of the same feature, respectively). For example, afeature of “taste” in food would be dependent on the feature of“ingredients” and the values thereof. Alternatively, the engine size(“feature”) of a car and the color (“feature”) of the car can beindependent from each other but the price (“feature”) of the car can bedependent on both the engine size and the color. Thus, Step 103 candefine inter-relationships of the feature values or define a primary oranchor value for the linked features (e.g., for “taste” having a valueof “sour”, a primary anchor value can be “Tamarind”).

Accordingly, Step 103 has the ability to define a proximal model ofitems depending upon the features and cognitive entity phrasing maps foreach attribute.

Step 104 determines if there is a direct (e.g., exact) match of an itemto the user based on the values of the features extracted by Step 103.That is, Step 104 would attempt to find a car (“item”) having a color,engine size and cost (“features”) of red, 2.0-cylinder, under 20,000dollars (“values”), respectively.

If “YES”, Step 106 returns the direct matched item to the user.

If “NO”, Step 105 calculates a closest item to the requested item by theuser having a lowest (e.g., smallest) proximal distance from therequested item (the details by Step 105 are shown in FIG. 2). That is,Step 105 a extracts the features of interest identified in Step 103 fromeach potential return item from a database 160 and Step 105 b extractsthe values of interest corresponding to the extracted features ofinterest. In other words, Step 105 a extracts color, engine size, andcost of all potential cars that can be a return item and Step 105 bextracts the values of each of the extracted features. The database 160includes potential return items.

It is noted that Step 105 a does not extract features not extracted byStep 103. That is, a car, for example, can be described by a plethora offeatures but Step 105 a extracts the features of interest (i.e., in thispresent example, features of interest would be color, engine size, andcost) corresponding to the user request to provide the closest item.Thus, features not of interest (e.g., type of seats, type oftransmission, type of tires, etc.) and values thereof are not extractedby Step 105 a/105 b.

Step 105 c optionally sorts the features based on a distinct userpreference or input weights to the features 150. That is, the user ranks(preferences) the features according to importance of the returned itemmatching the value of that feature. The distinct user preference orinput weights to the features 150 can include a pre-configuredpreference of the user for the features, an additional query to the userfrom the GUI for the user to weight each identified feature by Step 103,a learned preference based on past user selections of the returned item(e.g., user always picks a car returned that matches cost instead ofcolor), etc. For example, the user can assign that the feature of coloris three times as important as cost and twice as important as enginesize. It is noted that Step 105 c is optional and absent an affirmativeweight, by default, Step 105 ranks each feature equally.

Step 105 d queries for items fulfilling the value condition of thefeatures and ranks the returned items based on the lowest proximaldistance between the potential return item and the requested item.

Step 105 e continuously causes Step 105 d to loop to return potentialitems to the user as the user response 106. Also, if no potential returnitems are found within a threshold proximal distance, Step 105 e causesStep 105 d to find potential return items based on a similarity ofvalues of the potential return items to the request item. For example,if the extracted feature is color with a value of “red” for a car (item)and no red cars are in the database 160, Step 105 d can return items tothe user having a somewhat similar color such as “metallic red” (e.g.,Step 105 d can use a color scale to calculate similarity between red anda value of a potential return result). Thus, if the metallic red car isunder 20,000 dollars and has a 2.0-cylinder engine, Step 105 d canreturn the metallic red car to the user over a car that is, for example,green based on the preferences of the user. That is, Step 105 edynamically varies (overrides) the proximal distance driven by priorityfactors such that, for example, if a preferred flavor was “sour” and noprimary ingredient to give the taste of sour was in stock (e.g.,available), then Step 105 e can traverse to the next closest ingredientand anchor it (e.g., from tamarind to lemon). Also, since 105 e is afeedback loop, the user preferences (e.g., weights) are re-examined byStep 105 d such that if the closest alternative was unavailable as inthe above example and “lemon” was suggested, if the user was allergic tolemon, Step 105 e would again cause the next closest alternative to besuggested such as “lime juice”.

Thereby, Step 106 gives a ranked list of return items to the user rankedaccording to the proximal distance from the requested item.

In an exemplary use case of the method 100 as shown in FIG. 3, themethod 100 can recommend alternative meals for a user based on userpreference at a restaurant that does not have the requested meal.

For example, if a user normally orders “Sambar” (e.g., identified byStep 102), the method 100 via Step 103 breaks down the ingredients asfeatures and determines the values of the ingredients for “Sambar”. Asshown in FIG. 3, Sambar is broken to its atomic and compositeingredients and identifies its Anchor entity as Dhal. It is noted thatsome items, e.g. Hing Powder and Water are atomic and could not bebroken down further.

When Step 104 determines that no match exists, Step 105 searches forother available dishes and Step 105 a/105 b breaks down theiringredients and performs mapping of values to ingredients. In thisexample, Step 105 d picks up Dhal Tadkha and Dhal Tomato curry, asclosest dishes based on their Dhal content and the similarity of otheringredients. As shown in FIG. 3, proximates are flagged as H (high), M(medium) or L (low) based on their ability to map, e.g. H when the exactingredient was found in another dish, L when it was not found, and Mwhen a similar ingredient (e.g., from the same family) was found.However, a finer measure of similarity could be calculated, e.g. aspercentage based on ontology tree.

Based on user preferences factored in Step 105 c, Step 105 d eliminatesDhal Tadkha because the user (i.e., Jack) is allergic to Citric juice,and therefore suggests Dhal Tomato curry as the best proximate toreplace the initial Sambar order. The allergy to citric juice could beknow either directly from the user (e.g. as a note/input at the time oforder), or from the history of the user's orders, if he was a regularcustomer, the method 100 could have learned that some ingredients aresystematically avoided by the user and therefore would exclude them fromthe suggestions.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop circuits, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or circuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

As shown in FIG. 4, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing circuit. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the anti-counterfeiting system 100 and theanti-counterfeiting system 600 described herein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A cognitive proximate recommendation methodincluding a database, the cognitive proximate recommendation methodcomprising: identifying a requested item based on a user request; firstextracting a requested feature and a requested value of the requestedfeature for the requested item; and returning a return item from aplurality of return items stored in the database by: second extracting areturn feature corresponding to the requested feature for each of theplurality of return items; third extracting a return value of the returnfeature; and calculating a proximal distance between the return valuefor each of the plurality of return items and the requested value of therequested item, wherein the returning returns a ranked list of theplurality of return items ranked in order of the proximal distance,wherein the first extracting extracts a plurality of requested featuresand a requested value for each of the plurality of requested featuresbased on the user request, wherein the second extracting furtherextracts a plurality of return features for each return itemcorresponding to the requested feature of the requested item, sortingthe plurality of return features based on a learned weight from a userselection of the return item from the ranked list for each of the firstextracted plurality of requested features such that the calculatingcalculates the proximal distance based on the learned weight, whereinthe returning returns an updated ranked list based on the learnedweight, and wherein the updated ranked list further includesinter-relationships that are defined by defining a primary value of eachof the requested items and the return feature corresponding to therequested feature or anchor value for each of the requested items andthe return feature corresponding to the requested feature.
 2. Thecognitive proximate recommendation method of claim 1, wherein thereturning returns the return item from the plurality of return itemsstored in the database having a closest proximal distance to therequested item.
 3. The cognitive proximate recommendation method ofclaim 1, wherein the first extracting extracts the inter-relationshipsbetween each of the plurality of requested features.
 4. The cognitiveproximate recommendation method of claim 1, further comprising sortingthe plurality of return features based on a user input weight for eachof the first extracted plurality of requested features such that thecalculating calculates the proximal distance based on the user inputweight.
 5. The cognitive proximate recommendation method of claim 1,wherein the identifying identifies the requested item using adomain-specific taxonomy.
 6. The cognitive proximate recommendationmethod of claim 1, wherein the user request comprises a plurality ofwords, and wherein the first extracting extracts the requested featurefrom a semantic relationship between the plurality of words.
 7. Thecognitive proximate recommendation method of claim 1, wherein the returnitem always has a closest proximal distance if the requested valuematches the return value.
 8. The cognitive proximate recommendationmethod of claim 1, wherein the calculating calculates the proximaldistance based on a measurable similarity between the return value andthe requested value.
 9. The cognitive proximate recommendation method ofclaim 1, wherein the returning recommends a return item having a returnvalue having a measurable similarity to the requested value if the thirdextracting does not extract a matching return value.
 10. The cognitiveproximate recommendation method of claim 1, wherein each feature is oneof dependent or independent to other features of the requested item. 11.The cognitive proximate recommendation method of claim 10, wherein eachvalue of each feature is one of dependent or independent to other valuesof the requested item.
 12. The cognitive proximate recommendation methodof claim 1, wherein each value of each feature is one of dependent orindependent to other values of the requested item.
 13. A non-transitorycomputer-readable recording medium recording a cognitive proximaterecommendation program including a database, the cognitive proximaterecommendation program causing a computer to perform: identifying arequested item based on a user request; first extracting a requestedfeature and a requested value of the requested feature for the requesteditem; and returning a return item from a plurality of return itemsstored in the database by: second extracting a return featurecorresponding to the requested feature for each of the plurality ofreturn items; third extracting a return value of the return feature; andcalculating a proximal distance between the return value for each of theplurality of return items and the requested value of the requested item,wherein the returning returns a ranked list of the plurality of returnitems ranked in order of the proximal distance, wherein the firstextracting extracts a plurality of requested features and a requestedvalue for each of the plurality of requested features based on the userrequest, wherein the second extracting further extracts a plurality ofreturn features for each return item corresponding to the requestedfeature of the requested item, sorting the plurality of return featuresbased on a learned weight from a user selection of the return item fromthe ranked list for each of the first extracted plurality of requestedfeatures such that the calculating calculates the proximal distancebased on the learned weight, wherein the returning returns an updatedranked list based on the learned weight, and wherein the updated rankedlist further includes inter-relationships that are defined by defining aprimary value of each of the requested items and the return featurecorresponding to the requested feature or anchor value for each of therequested items and the return feature corresponding to the requestedfeature.
 14. The cognitive proximate recommendation program of claim 13,wherein each feature is one of dependent or independent to otherfeatures of the requested item.
 15. The cognitive proximaterecommendation program of claim 13, wherein each value of each featureis one of dependent or independent to other values of the requesteditem.
 16. The cognitive proximate recommendation program of claim 15,wherein each value of each feature is one of dependent or independent toother values of the requested item.
 17. A cognitive proximaterecommendation system, the cognitive proximate recommendation systemcomprising: a database; a processor; and a memory, the memory storinginstructions to cause the processor to: identify a requested item basedon a user request; first extract a requested feature and a requestedvalue of the requested feature for the requested item; and return areturn item from a plurality of return items stored in the database by:second extracting a return feature corresponding to the requestedfeature for each of the plurality of return items; third extracting areturn value of the return feature; and calculating a proximal distancebetween the return value for each of the plurality of return items andthe requested value of the requested item, wherein the returning returnsa ranked list of the plurality of return items ranked in order of theproximal distance, wherein the first extracting extracts a plurality ofrequested features and a requested value for each of the plurality ofrequested features based on the user request, wherein the secondextracting further extracts a plurality of return features for eachreturn item corresponding to the requested feature of the requesteditem, sorting the plurality of return features based on a learned weightfrom a user selection of the return item from the ranked list for eachof the first extracted plurality of requested features such that thecalculating calculates the proximal distance based on the learnedweight, wherein the returning returns an updated ranked list based onthe learned weight, and wherein the updated ranked list further includesinter-relationships that are defined by defining a primary value of eachof the requested items and the return feature corresponding to therequested feature or anchor value for each of the requested items andthe return feature corresponding to the requested feature.
 18. Thecognitive proximate recommendation system of claim 17, wherein thereturning returns the return item from the plurality of return itemsstored in the database having a closest proximal distance to therequested item.
 19. The cognitive proximate recommendation system ofclaim 17, wherein each feature is one of dependent or independent toother features of the requested item.
 20. The cognitive proximaterecommendation system of claim 17, wherein each value of each feature isone of dependent or independent to other values of the requested item.